Problems

We vibe coded it and now it can’t scale

Vibe coding can get you to a demo. It can’t yet get you to production. Connecting to real repositories, securing it, and telling the difference between code that looks right and code that actually works still takes a senior engineer.

Which of these happened?

  • It worked in the demo and broke with real users
  • No one can explain how it works
  • Adding a feature breaks something else
  • No tests, no documentation, no clear owner
  • Security was never part of the prompt

Why it happens

AI is not there yet. Maybe in a few years. But right now, the gap between a vibe-coded app and production software still needs a senior human engineer.

Some of the best-known founders in tech say all you need to know is how to vibe code, and your app is ready to go. For a prototype, that can be true. For software that has to connect to real systems, protect real data and survive real users, it isn’t, and the published research says so.

[ DEV_SLOWDOWN ]
19%
Slower: experienced developers using AI on their own complex codebases, while believing they were faster.
[ SECURITY_FAIL ]
44%
Of AI-generated code still fails security tests, essentially unchanged in a year.
[ STABILITY_DROP ]
7.2%
Drop in software delivery stability as AI adoption rises.

Sources: METR randomized controlled trial (2025, early-2025 AI tools); Veracode 2026 GenAI Code Security Report; Google Cloud DORA Report (2024).

Vibe coding has real uses: prototypes, internal tools, testing an idea fast. The mess starts when the prototype quietly becomes the product.

What I do

Find out what is sound and what isn’t, then refactor it, rebuild it on a real foundation, or replace it, built so someone can own it. AI-accelerated development works when a senior engineer owns the architecture.

1,039Merged PRs in 90 days from a 3.25-person team, about 8× the conventional benchmark.

FAQ

Is vibe coding ever the right call?

Yes, for prototypes, internal tools and testing ideas. It’s the wrong foundation for production software that has to scale.

Do we have to start over?

Not necessarily. The diagnostic shows what can stay.

Will AI close this gap?

Probably, in time. Today, the published research says it hasn’t.